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Do apolipoproteins improve coronary risk prediction in subjects with metabolic syndrome? Insights from the North Italian Brianza cohort study.

Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy. Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy; Research Centre on Dyslipidemia, Department of Clinical and Experimental Medicine, University of Insubria, Viale Borri 57, 21100 Varese, Italy. Department of Biostatistics, University of North Carolina at Chapel Hill, 137 E Franklin Street, Chapel Hill, NC 27514, USA. Department of Health Sciences, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy. IRCCS Istituto Auxologico Italiano, University of Milano-Bicocca, Milano, Italy. Research Centre in Epidemiology and Preventive Medicine - EPIMED, Department of Clinical and Experimental Medicine, University of Insubria, Via O Rossi 9, 21100 Varese, Italy. Electronic address: marco.ferrario@uninsubria.it.

Atherosclerosis. 2014;(1):175-81
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Abstract

OBJECTIVE We assessed predictive abilities and clinical utility of CVD risk algorithms including ApoB and ApoAI among non-diabetic subjects with metabolic syndrome (MetS). METHODS Three independent population-based cohorts (3677 35-74 years old) were enrolled in Northern Italy, adopting standardized MONICA procedures. Through Cox models, we assessed the associations between lipid measures and first coronary events, as well as the changes in discrimination and reclassification (NRI) when standard lipids or apolipoproteins were added to the CVD risk algorithm including non-lipids risk factors. Finally, the best models including lipids or apolipoproteins were compared. RESULTS During the 14.5 years median follow-up time, 164 coronary events were validated. All measures showed statistically significant associations with the endpoint, while in the MetS subgroup HDL-C and ApoAI (men, HR = 1.59; 95%CI: 0.96-2.65) were not associated. Models including HDL-C plus TC and ApoB plus ApoAI for lipids and apolipoproteins, respectively, showed the best predictive values. When ApoB plus ApoAI replaced TC plus HDL-C, NRI values improved in subjects with MetS (13.8; CI95%: -5.1,53.1), significantly in those previously classified at intermediate risk (44.5; CI95% 13.8,129.6). In this subgroup, 5.5% of subjects was moved in the high (40.0% of expected events) and 17.0% in the low risk class (none had an event at 10 years). CONCLUSIONS ApoB and ApoAI could improve coronary risk prediction when used as second level biomarkers in non-diabetic subjects with MetS classified at intermediate risk. The absence of cases moved downward suggests the gain in avoiding treatments in non-cases and favor the use of apolipoproteins for risk assessment.